Godaddy Microbusiness Forecasting

# Set the working directory
setwd("/Users/dreamer/Downloads/Godaddy/godaddy_microbusiness_forecasting")

# Importing the libraries
library(tidyverse)

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## ✔ ggplot2   3.4.1     ✔ tibble    3.1.8
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
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# ggplot2, purrr, tibble, dplyr, tidyr, stringr, readr, forcats
library(mice)

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library(maps)

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#library(ggplot2)
library(gridExtra)

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#library(dplyr)
library(caret)

## Loading required package: lattice
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library(gbm)

## Loaded gbm 2.1.8.1

#library(png)
#library(ggmap)
library(viridis)

## Loading required package: viridisLite
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library(mapdata)
library(corrplot)

## corrplot 0.92 loaded

# Code starts here

Exploring the datasets

Explore the datasets to get a better understanding of the data.
Load the train and test datasets into dataframes.

# Load train.csv into a dataframe
train_df <- read.csv("./datasets/train.csv")

# Load test.csv into a dataframe
test_df <- read.csv("./datasets/test.csv")

# Load census_starter.csv into a dataframe
census_df <- read.csv("./datasets/census_starter.csv")

Check the dataframes

After reading the CSV files into dataframes, we should check whether the data is loaded correctly or not. We can use the head() function of R to display the first few rows of the dataframes and tail() function to display the last rows. This will display the first and last 10 rows of the train, test and census dataframes. We can also use other pandas functions such as info() and describe() to get more information about the dataframes, such as column names, data types, and summary statistics.

# Display the first 10 rows of the dataframes
head(train_df, n = 10)

##             row_id cfips         county   state first_day_of_month
## 1  1001_2019-08-01  1001 Autauga County Alabama         2019-08-01
## 2  1001_2019-09-01  1001 Autauga County Alabama         2019-09-01
## 3  1001_2019-10-01  1001 Autauga County Alabama         2019-10-01
## 4  1001_2019-11-01  1001 Autauga County Alabama         2019-11-01
## 5  1001_2019-12-01  1001 Autauga County Alabama         2019-12-01
## 6  1001_2020-01-01  1001 Autauga County Alabama         2020-01-01
## 7  1001_2020-02-01  1001 Autauga County Alabama         2020-02-01
## 8  1001_2020-03-01  1001 Autauga County Alabama         2020-03-01
## 9  1001_2020-04-01  1001 Autauga County Alabama         2020-04-01
## 10 1001_2020-05-01  1001 Autauga County Alabama         2020-05-01
##    microbusiness_density active
## 1               3.007682   1249
## 2               2.884870   1198
## 3               3.055843   1269
## 4               2.993233   1243
## 5               2.993233   1243
## 6               2.969090   1242
## 7               2.909326   1217
## 8               2.933231   1227
## 9               3.000167   1255
## 10              3.004948   1257

head(test_df, n = 10)

##             row_id cfips first_day_of_month
## 1  1001_2022-11-01  1001         2022-11-01
## 2  1003_2022-11-01  1003         2022-11-01
## 3  1005_2022-11-01  1005         2022-11-01
## 4  1007_2022-11-01  1007         2022-11-01
## 5  1009_2022-11-01  1009         2022-11-01
## 6  1011_2022-11-01  1011         2022-11-01
## 7  1013_2022-11-01  1013         2022-11-01
## 8  1015_2022-11-01  1015         2022-11-01
## 9  1017_2022-11-01  1017         2022-11-01
## 10 1019_2022-11-01  1019         2022-11-01

head(census_df, n = 10)

##    pct_bb_2017 pct_bb_2018 pct_bb_2019 pct_bb_2020 pct_bb_2021 cfips
## 1         76.6        78.9        80.6        82.7        85.5  1001
## 2         74.5        78.1        81.8        85.1        87.9  1003
## 3         57.2        60.4        60.5        64.6        64.6  1005
## 4         62.0        66.1        69.2        76.1        74.6  1007
## 5         65.8        68.5        73.0        79.6        81.0  1009
## 6         49.4        58.9        60.1        60.6        59.4  1011
## 7         58.2        62.1        64.6        73.6        76.3  1013
## 8         71.0        73.0        75.1        79.8        81.6  1015
## 9         62.8        66.5        69.4        74.5        77.1  1017
## 10        67.5        68.6        70.7        75.0        76.7  1019
##    pct_college_2017 pct_college_2018 pct_college_2019 pct_college_2020
## 1              14.5             15.9             16.1             16.7
## 2              20.4             20.7             21.0             20.2
## 3               7.6              7.8              7.6              7.3
## 4               8.1              7.6              6.5              7.4
## 5               8.7              8.1              8.6              8.9
## 6               6.6              7.4              7.4              6.1
## 7               9.6              9.7              9.7             10.1
## 8              10.2             10.2             10.5             10.5
## 9               9.0              9.3              9.5             10.5
## 10              6.6              6.8              6.6              6.3
##    pct_college_2021 pct_foreign_born_2017 pct_foreign_born_2018
## 1              16.4                   2.1                   2.0
## 2              20.6                   3.2                   3.4
## 3               6.7                   2.7                   2.5
## 4               7.9                   1.0                   1.4
## 5               9.3                   4.5                   4.4
## 6               8.1                   1.8                   0.9
## 7               8.1                   1.0                   1.4
## 8              11.4                   2.6                   2.7
## 9               9.6                   1.3                   1.4
## 10              6.2                   0.7                   0.8
##    pct_foreign_born_2019 pct_foreign_born_2020 pct_foreign_born_2021
## 1                    2.3                   2.3                   2.1
## 2                    3.7                   3.4                   3.5
## 3                    2.7                   2.6                   2.6
## 4                    1.5                   1.6                   1.1
## 5                    4.5                   4.4                   4.5
## 6                    0.7                   1.5                   1.2
## 7                    0.8                   1.9                   1.7
## 8                    2.7                   2.5                   2.5
## 9                    1.8                   1.9                   2.0
## 10                   0.9                   1.9                   2.0
##    pct_it_workers_2017 pct_it_workers_2018 pct_it_workers_2019
## 1                  1.3                 1.1                 0.7
## 2                  1.4                 1.3                 1.4
## 3                  0.5                 0.3                 0.8
## 4                  1.2                 1.4                 1.6
## 5                  1.3                 1.4                 0.9
## 6                  0.4                 0.3                 0.5
## 7                  1.1                 1.4                 1.7
## 8                  1.4                 1.4                 1.2
## 9                  2.4                 2.1                 2.1
## 10                 1.4                 1.3                 1.2
##    pct_it_workers_2020 pct_it_workers_2021 median_hh_inc_2017
## 1                  0.6                 1.1              55317
## 2                  1.0                 1.3              52562
## 3                  1.1                 0.8              33368
## 4                  1.7                 2.1              43404
## 5                  1.1                 0.9              47412
## 6                  0.3                 0.2              29655
## 7                  1.3                 1.4              36326
## 8                  1.0                 1.0              43686
## 9                  2.3                 1.8              37342
## 10                 0.9                 0.4              40041
##    median_hh_inc_2018 median_hh_inc_2019 median_hh_inc_2020 median_hh_inc_2021
## 1               58786              58731              57982              62660
## 2               55962              58320              61756              64346
## 3               34186              32525              34990              36422
## 4               45340              47542              51721              54277
## 5               48695              49358              48922              52830
## 6               32152              37785              33866              29063
## 7               39109              40688              44850              45236
## 8               45197              47255              50128              50977
## 9               39872              42289              43875              47232
## 10              41014              41919              42509              43475

# Display the last 10 rows of the dataframes
tail(train_df, n = 10)

##                  row_id cfips        county   state first_day_of_month
## 122256 56045_2022-01-01 56045 Weston County Wyoming         2022-01-01
## 122257 56045_2022-02-01 56045 Weston County Wyoming         2022-02-01
## 122258 56045_2022-03-01 56045 Weston County Wyoming         2022-03-01
## 122259 56045_2022-04-01 56045 Weston County Wyoming         2022-04-01
## 122260 56045_2022-05-01 56045 Weston County Wyoming         2022-05-01
## 122261 56045_2022-06-01 56045 Weston County Wyoming         2022-06-01
## 122262 56045_2022-07-01 56045 Weston County Wyoming         2022-07-01
## 122263 56045_2022-08-01 56045 Weston County Wyoming         2022-08-01
## 122264 56045_2022-09-01 56045 Weston County Wyoming         2022-09-01
## 122265 56045_2022-10-01 56045 Weston County Wyoming         2022-10-01
##        microbusiness_density active
## 122256              1.749688     98
## 122257              1.749688     98
## 122258              1.767542     99
## 122259              1.767542     99
## 122260              1.803249    101
## 122261              1.803249    101
## 122262              1.803249    101
## 122263              1.785395    100
## 122264              1.785395    100
## 122265              1.785395    100

tail(test_df, n = 10)

##                 row_id cfips first_day_of_month
## 25071 56027_2023-06-01 56027         2023-06-01
## 25072 56029_2023-06-01 56029         2023-06-01
## 25073 56031_2023-06-01 56031         2023-06-01
## 25074 56033_2023-06-01 56033         2023-06-01
## 25075 56035_2023-06-01 56035         2023-06-01
## 25076 56037_2023-06-01 56037         2023-06-01
## 25077 56039_2023-06-01 56039         2023-06-01
## 25078 56041_2023-06-01 56041         2023-06-01
## 25079 56043_2023-06-01 56043         2023-06-01
## 25080 56045_2023-06-01 56045         2023-06-01

tail(census_df, n = 10)

##      pct_bb_2017 pct_bb_2018 pct_bb_2019 pct_bb_2020 pct_bb_2021 cfips
## 3133        67.8        73.3        72.2        77.6        74.3 56027
## 3134        80.4        82.2        85.7        86.7        87.9 56029
## 3135        68.4        69.2        70.9        74.5        75.1 56031
## 3136        80.9        81.1        82.9        82.8        84.7 56033
## 3137        82.9        81.7        85.6        88.1        89.8 56035
## 3138        82.2        82.4        84.0        86.7        88.4 56037
## 3139        83.5        85.9        87.1        89.1        90.5 56039
## 3140        83.8        88.2        89.5        91.4        90.6 56041
## 3141        76.4        78.3        78.2        82.8        85.4 56043
## 3142        71.1        73.3        76.8        79.7        81.3 56045
##      pct_college_2017 pct_college_2018 pct_college_2019 pct_college_2020
## 3133             14.2             13.0             14.5             14.7
## 3134             19.2             19.1             21.5             21.8
## 3135             14.6             14.1             10.9             10.8
## 3136             18.3             16.2             16.5             16.8
## 3137             19.2             19.0             16.7             21.7
## 3138             15.3             15.2             14.8             13.7
## 3139             37.7             37.8             38.9             37.2
## 3140             11.9             10.5             11.1             12.6
## 3141             15.4             15.0             15.4             15.0
## 3142             14.1             13.5             13.4             12.7
##      pct_college_2021 pct_foreign_born_2017 pct_foreign_born_2018
## 3133             17.1                   0.3                   0.2
## 3134             22.0                   2.4                   2.5
## 3135             10.6                   3.0                   2.8
## 3136             18.0                   2.3                   2.1
## 3137             20.9                   3.9                   3.1
## 3138             12.4                   5.0                   5.3
## 3139             38.3                  10.8                  11.2
## 3140             12.3                   2.9                   3.1
## 3141             17.2                   2.3                   1.4
## 3142             13.9                   3.8                   4.1
##      pct_foreign_born_2019 pct_foreign_born_2020 pct_foreign_born_2021
## 3133                   0.3                   0.0                   0.0
## 3134                   2.5                   2.4                   2.5
## 3135                   3.0                   2.6                   2.0
## 3136                   2.0                   2.5                   2.2
## 3137                   4.4                   5.1                   5.1
## 3138                   4.7                   5.2                   5.5
## 3139                  11.8                  11.4                  11.1
## 3140                   2.9                   2.9                   2.9
## 3141                   1.6                   2.2                   1.0
## 3142                   1.7                   2.3                   1.6
##      pct_it_workers_2017 pct_it_workers_2018 pct_it_workers_2019
## 3133                 1.2                 1.0                 0.9
## 3134                 1.7                 2.1                 2.6
## 3135                 0.0                 0.0                 0.0
## 3136                 1.6                 1.7                 1.5
## 3137                 0.1                 0.0                 0.0
## 3138                 0.6                 0.6                 1.0
## 3139                 0.7                 1.2                 1.4
## 3140                 1.2                 1.2                 1.4
## 3141                 1.3                 1.0                 0.9
## 3142                 0.6                 0.6                 0.0
##      pct_it_workers_2020 pct_it_workers_2021 median_hh_inc_2017
## 3133                 0.0                 1.9              36793
## 3134                 1.9                 1.7              60828
## 3135                 0.0                 0.0              47380
## 3136                 1.8                 1.3              56455
## 3137                 0.0                 0.0              84911
## 3138                 0.9                 1.0              71083
## 3139                 1.5                 2.0              80049
## 3140                 1.7                 0.9              54672
## 3141                 0.9                 1.1              51362
## 3142                 0.0                 0.0              59605
##      median_hh_inc_2018 median_hh_inc_2019 median_hh_inc_2020
## 3133              39359              39150              46111
## 3134              62666              63582              63684
## 3135              47096              50903              57784
## 3136              58521              60807              59380
## 3137              78680              77403              78655
## 3138              73008              74843              73384
## 3139              83831              84678              87053
## 3140              58235              63403              72458
## 3141              53426              54158              57306
## 3142              52867              57031              53333
##      median_hh_inc_2021
## 3133              48688
## 3134              62586
## 3135              59402
## 3136              62531
## 3137              82342
## 3138              76668
## 3139              94498
## 3140              75106
## 3141              62271
## 3142              65566

# Display information about the train dataframe
str(train_df)

## 'data.frame':    122265 obs. of  7 variables:
##  $ row_id               : chr  "1001_2019-08-01" "1001_2019-09-01" "1001_2019-10-01" "1001_2019-11-01" ...
##  $ cfips                : int  1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
##  $ county               : chr  "Autauga County" "Autauga County" "Autauga County" "Autauga County" ...
##  $ state                : chr  "Alabama" "Alabama" "Alabama" "Alabama" ...
##  $ first_day_of_month   : chr  "2019-08-01" "2019-09-01" "2019-10-01" "2019-11-01" ...
##  $ microbusiness_density: num  3.01 2.88 3.06 2.99 2.99 ...
##  $ active               : int  1249 1198 1269 1243 1243 1242 1217 1227 1255 1257 ...

cat(rep("=", 40), "\n") # Print a line of 40 equal signs

## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

summary(train_df)

##     row_id              cfips          county             state          
##  Length:122265      Min.   : 1001   Length:122265      Length:122265     
##  Class :character   1st Qu.:18177   Class :character   Class :character  
##  Mode  :character   Median :29173   Mode  :character   Mode  :character  
##                     Mean   :30376                                        
##                     3rd Qu.:45077                                        
##                     Max.   :56045                                        
##  first_day_of_month microbusiness_density     active       
##  Length:122265      Min.   :  0.000       Min.   :      0  
##  Class :character   1st Qu.:  1.639       1st Qu.:    145  
##  Mode  :character   Median :  2.587       Median :    488  
##                     Mean   :  3.818       Mean   :   6443  
##                     3rd Qu.:  4.519       3rd Qu.:   2124  
##                     Max.   :284.340       Max.   :1167744

# Display information about the test dataframe
str(test_df)

## 'data.frame':    25080 obs. of  3 variables:
##  $ row_id            : chr  "1001_2022-11-01" "1003_2022-11-01" "1005_2022-11-01" "1007_2022-11-01" ...
##  $ cfips             : int  1001 1003 1005 1007 1009 1011 1013 1015 1017 1019 ...
##  $ first_day_of_month: chr  "2022-11-01" "2022-11-01" "2022-11-01" "2022-11-01" ...

cat(rep("=", 40), "\n") # Print a line of 40 equal signs

## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

summary(test_df)

##     row_id              cfips       first_day_of_month
##  Length:25080       Min.   : 1001   Length:25080      
##  Class :character   1st Qu.:18177   Class :character  
##  Mode  :character   Median :29173   Mode  :character  
##                     Mean   :30376                     
##                     3rd Qu.:45077                     
##                     Max.   :56045

# Display information about the census dataframe
str(census_df)

## 'data.frame':    3142 obs. of  26 variables:
##  $ pct_bb_2017          : num  76.6 74.5 57.2 62 65.8 49.4 58.2 71 62.8 67.5 ...
##  $ pct_bb_2018          : num  78.9 78.1 60.4 66.1 68.5 58.9 62.1 73 66.5 68.6 ...
##  $ pct_bb_2019          : num  80.6 81.8 60.5 69.2 73 60.1 64.6 75.1 69.4 70.7 ...
##  $ pct_bb_2020          : num  82.7 85.1 64.6 76.1 79.6 60.6 73.6 79.8 74.5 75 ...
##  $ pct_bb_2021          : num  85.5 87.9 64.6 74.6 81 59.4 76.3 81.6 77.1 76.7 ...
##  $ cfips                : int  1001 1003 1005 1007 1009 1011 1013 1015 1017 1019 ...
##  $ pct_college_2017     : num  14.5 20.4 7.6 8.1 8.7 6.6 9.6 10.2 9 6.6 ...
##  $ pct_college_2018     : num  15.9 20.7 7.8 7.6 8.1 7.4 9.7 10.2 9.3 6.8 ...
##  $ pct_college_2019     : num  16.1 21 7.6 6.5 8.6 7.4 9.7 10.5 9.5 6.6 ...
##  $ pct_college_2020     : num  16.7 20.2 7.3 7.4 8.9 6.1 10.1 10.5 10.5 6.3 ...
##  $ pct_college_2021     : num  16.4 20.6 6.7 7.9 9.3 8.1 8.1 11.4 9.6 6.2 ...
##  $ pct_foreign_born_2017: num  2.1 3.2 2.7 1 4.5 1.8 1 2.6 1.3 0.7 ...
##  $ pct_foreign_born_2018: num  2 3.4 2.5 1.4 4.4 0.9 1.4 2.7 1.4 0.8 ...
##  $ pct_foreign_born_2019: num  2.3 3.7 2.7 1.5 4.5 0.7 0.8 2.7 1.8 0.9 ...
##  $ pct_foreign_born_2020: num  2.3 3.4 2.6 1.6 4.4 1.5 1.9 2.5 1.9 1.9 ...
##  $ pct_foreign_born_2021: num  2.1 3.5 2.6 1.1 4.5 1.2 1.7 2.5 2 2 ...
##  $ pct_it_workers_2017  : num  1.3 1.4 0.5 1.2 1.3 0.4 1.1 1.4 2.4 1.4 ...
##  $ pct_it_workers_2018  : num  1.1 1.3 0.3 1.4 1.4 0.3 1.4 1.4 2.1 1.3 ...
##  $ pct_it_workers_2019  : num  0.7 1.4 0.8 1.6 0.9 0.5 1.7 1.2 2.1 1.2 ...
##  $ pct_it_workers_2020  : num  0.6 1 1.1 1.7 1.1 0.3 1.3 1 2.3 0.9 ...
##  $ pct_it_workers_2021  : num  1.1 1.3 0.8 2.1 0.9 0.2 1.4 1 1.8 0.4 ...
##  $ median_hh_inc_2017   : int  55317 52562 33368 43404 47412 29655 36326 43686 37342 40041 ...
##  $ median_hh_inc_2018   : num  58786 55962 34186 45340 48695 ...
##  $ median_hh_inc_2019   : int  58731 58320 32525 47542 49358 37785 40688 47255 42289 41919 ...
##  $ median_hh_inc_2020   : num  57982 61756 34990 51721 48922 ...
##  $ median_hh_inc_2021   : num  62660 64346 36422 54277 52830 ...

cat(rep("=", 40), "\n") # Print a line of 40 equal signs

## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

summary(census_df)

##   pct_bb_2017     pct_bb_2018     pct_bb_2019     pct_bb_2020   
##  Min.   :24.50   Min.   :25.70   Min.   :34.80   Min.   :33.30  
##  1st Qu.:64.20   1st Qu.:67.42   1st Qu.:70.50   1st Qu.:74.10  
##  Median :70.70   Median :73.60   Median :76.45   Median :79.60  
##  Mean   :69.92   Mean   :72.69   Mean   :75.40   Mean   :78.54  
##  3rd Qu.:76.40   3rd Qu.:78.80   3rd Qu.:81.40   3rd Qu.:84.10  
##  Max.   :94.60   Max.   :95.50   Max.   :96.00   Max.   :97.10  
##                                                  NA's   :1      
##   pct_bb_2021        cfips       pct_college_2017 pct_college_2018
##  Min.   :37.00   Min.   : 1001   Min.   : 2.40    Min.   : 0.00   
##  1st Qu.:76.40   1st Qu.:18178   1st Qu.: 9.70    1st Qu.: 9.90   
##  Median :81.70   Median :29176   Median :12.80    Median :13.00   
##  Mean   :80.54   Mean   :30384   Mean   :13.81    Mean   :14.01   
##  3rd Qu.:85.90   3rd Qu.:45080   3rd Qu.:16.80    3rd Qu.:17.10   
##  Max.   :97.60   Max.   :56045   Max.   :43.70    Max.   :48.00   
##  NA's   :1                                                        
##  pct_college_2019 pct_college_2020 pct_college_2021 pct_foreign_born_2017
##  Min.   : 0.00    Min.   : 0.00    Min.   : 0.00    Min.   : 0.000       
##  1st Qu.:10.10    1st Qu.:10.50    1st Qu.:10.60    1st Qu.: 1.400       
##  Median :13.25    Median :13.60    Median :13.80    Median : 2.700       
##  Mean   :14.24    Mean   :14.63    Mean   :14.85    Mean   : 4.702       
##  3rd Qu.:17.30    3rd Qu.:17.90    3rd Qu.:18.00    3rd Qu.: 5.700       
##  Max.   :45.40    Max.   :43.00    Max.   :43.70    Max.   :52.900       
##                   NA's   :1        NA's   :1                             
##  pct_foreign_born_2018 pct_foreign_born_2019 pct_foreign_born_2020
##  Min.   : 0.000        Min.   : 0.000        Min.   : 0.000       
##  1st Qu.: 1.400        1st Qu.: 1.400        1st Qu.: 1.400       
##  Median : 2.700        Median : 2.700        Median : 2.800       
##  Mean   : 4.725        Mean   : 4.769        Mean   : 4.749       
##  3rd Qu.: 5.700        3rd Qu.: 5.700        3rd Qu.: 5.700       
##  Max.   :53.300        Max.   :53.700        Max.   :54.000       
##                                              NA's   :1            
##  pct_foreign_born_2021 pct_it_workers_2017 pct_it_workers_2018
##  Min.   : 0.000        Min.   : 0.000      Min.   : 0.000     
##  1st Qu.: 1.400        1st Qu.: 0.800      1st Qu.: 0.800     
##  Median : 2.700        Median : 1.300      Median : 1.300     
##  Mean   : 4.744        Mean   : 1.427      Mean   : 1.382     
##  3rd Qu.: 5.700        3rd Qu.: 1.900      3rd Qu.: 1.800     
##  Max.   :54.000        Max.   :17.400      Max.   :11.700     
##  NA's   :1                                 NA's   :1          
##  pct_it_workers_2019 pct_it_workers_2020 pct_it_workers_2021 median_hh_inc_2017
##  Min.   : 0.000      Min.   : 0.000      Min.   : 0.000      Min.   : 19264    
##  1st Qu.: 0.700      1st Qu.: 0.700      1st Qu.: 0.600      1st Qu.: 41123    
##  Median : 1.200      Median : 1.200      Median : 1.100      Median : 48066    
##  Mean   : 1.339      Mean   : 1.309      Mean   : 1.273      Mean   : 49754    
##  3rd Qu.: 1.800      3rd Qu.: 1.800      3rd Qu.: 1.700      3rd Qu.: 55764    
##  Max.   :10.500      Max.   :15.200      Max.   :15.200      Max.   :129588    
##                      NA's   :1           NA's   :1                             
##  median_hh_inc_2018 median_hh_inc_2019 median_hh_inc_2020 median_hh_inc_2021
##  Min.   : 20188     Min.   : 21504     Min.   : 22292     Min.   : 17109    
##  1st Qu.: 42480     1st Qu.: 44155     1st Qu.: 45653     1st Qu.: 48180    
##  Median : 49888     Median : 51758     Median : 52842     Median : 55907    
##  Mean   : 51583     Mean   : 53476     Mean   : 55012     Mean   : 58223    
##  3rd Qu.: 57611     3rd Qu.: 59867     3rd Qu.: 61501     3rd Qu.: 64930    
##  Max.   :136268     Max.   :142299     Max.   :147111     Max.   :156821    
##  NA's   :1                             NA's   :2          NA's   :2

The is.na() function is used to create a logical matrix where TRUE represents a missing value and FALSE represents a non-missing value. The colSums() function is then used to count the number of missing values in each column of the data frame. If the sum of a column is greater than 0, it means that there is at least one missing value in that column.

# Check for missing values in the train data frame
colSums(is.na(train_df))

##                row_id                 cfips                county 
##                     0                     0                     0 
##                 state    first_day_of_month microbusiness_density 
##                     0                     0                     0 
##                active 
##                     0

#{r fig.width=7, fig.align='center', fig.height=4, out.width='100%'}
# Calculate the number and percentage of missing values for each column
missing_data <- census_df %>%
  summarise_all(~ sum(is.na(.))) %>%
  gather(variable, missing_count) %>%
  mutate(missing_percent = missing_count/nrow(census_df)*100)

# Create two plots side by side
plot1 <- ggplot(missing_data, aes(x = missing_count, y = variable)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  labs(x = "Number of missing values", y = "") +
  ggtitle("Number of missing values in census_df")

plot2 <- ggplot(missing_data, aes(x = missing_percent, y = variable)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  labs(x = "Percentage of missing values", y = "") +
  ggtitle("Percentage of missing values in census_df")

# Arrange the two plots side by side
grid.arrange(plot1, plot2, ncol = 2)

# Check for missing values in the test data frame
colSums(is.na(test_df))

##             row_id              cfips first_day_of_month 
##                  0                  0                  0

# Check for missing values in the census data frame
colSums(is.na(census_df))

##           pct_bb_2017           pct_bb_2018           pct_bb_2019 
##                     0                     0                     0 
##           pct_bb_2020           pct_bb_2021                 cfips 
##                     1                     1                     0 
##      pct_college_2017      pct_college_2018      pct_college_2019 
##                     0                     0                     0 
##      pct_college_2020      pct_college_2021 pct_foreign_born_2017 
##                     1                     1                     0 
## pct_foreign_born_2018 pct_foreign_born_2019 pct_foreign_born_2020 
##                     0                     0                     1 
## pct_foreign_born_2021   pct_it_workers_2017   pct_it_workers_2018 
##                     1                     0                     1 
##   pct_it_workers_2019   pct_it_workers_2020   pct_it_workers_2021 
##                     0                     1                     1 
##    median_hh_inc_2017    median_hh_inc_2018    median_hh_inc_2019 
##                     0                     1                     0 
##    median_hh_inc_2020    median_hh_inc_2021 
##                     2                     2

We use the complete.cases() function to determine which rows have complete data and which rows have missing values. The complete.cases() function returns a logical vector indicating which rows have no missing values. Therefore, to identify the rows with missing values, we use the ! operator to negate the logical vector returned by complete.cases(). Then, we use the is.na() function to identify which columns have missing values for each missing row:

# Identify rows with missing values in census_df
missing_rows <- which(!complete.cases(census_df))

# Identify columns with missing values for each missing row
for (i in missing_rows) {
  cat("Row", i, "has missing values in columns:",
      paste(names(census_df)[is.na(census_df[i,])], collapse = ", "), "\n")
}

## Row 93 has missing values in columns: pct_bb_2020, pct_bb_2021, pct_college_2020, pct_college_2021, pct_foreign_born_2020, pct_foreign_born_2021, pct_it_workers_2020, pct_it_workers_2021, median_hh_inc_2020, median_hh_inc_2021 
## Row 1817 has missing values in columns: pct_it_workers_2018, median_hh_inc_2018 
## Row 2645 has missing values in columns: median_hh_inc_2020 
## Row 2674 has missing values in columns: median_hh_inc_2021

print(census_df[missing_rows,])

##      pct_bb_2017 pct_bb_2018 pct_bb_2019 pct_bb_2020 pct_bb_2021 cfips
## 93          80.5        79.1        80.4          NA          NA  2261
## 1817        49.1        52.1        57.6        60.7        63.5 35039
## 2645        66.3        66.6        61.2        63.2        70.1 48243
## 2674        64.5        72.7        73.3        96.8        97.0 48301
##      pct_college_2017 pct_college_2018 pct_college_2019 pct_college_2020
## 93               23.1             19.0             16.5               NA
## 1817             12.0             12.5             12.6             10.6
## 2645             18.4             16.0             10.8             14.3
## 2674              4.7              0.0              0.0              0.0
##      pct_college_2021 pct_foreign_born_2017 pct_foreign_born_2018
## 93                 NA                   4.9                   6.3
## 1817             10.1                   4.5                   3.7
## 2645             10.9                  22.4                  14.9
## 2674              0.0                  10.8                  15.7
##      pct_foreign_born_2019 pct_foreign_born_2020 pct_foreign_born_2021
## 93                     6.6                    NA                    NA
## 1817                   4.2                   4.5                   4.8
## 2645                  20.9                  10.1                  12.7
## 2674                  12.2                   0.0                   1.2
##      pct_it_workers_2017 pct_it_workers_2018 pct_it_workers_2019
## 93                   3.3                 3.9                 5.3
## 1817                 0.8                  NA                 0.8
## 2645                 0.0                 0.0                 0.0
## 2674                 0.0                 0.0                 0.0
##      pct_it_workers_2020 pct_it_workers_2021 median_hh_inc_2017
## 93                    NA                  NA              86019
## 1817                 0.4                 0.7              33422
## 2645                 0.0                 0.0              46534
## 2674                 0.0                 0.0              80938
##      median_hh_inc_2018 median_hh_inc_2019 median_hh_inc_2020
## 93                82306              79867                 NA
## 1817                 NA              39952              42264
## 2645              53194              53088                 NA
## 2674              81875              83750              44076
##      median_hh_inc_2021
## 93                   NA
## 1817              46994
## 2645              38659
## 2674                 NA

We use the mice package to impute missing values in the census_df dataframe.

m: the number of imputations to generate was set to 5, because, generally, m should be set to at least 5 for good imputation performance. creating too many datasets will increase the computational cost and may not necessarily lead to better results.

maxit: maxit was set to 50 to allow for a larger number of iterations to ensure that the imputation algorithm converges and fills in missing values as accurately as possible.

method: In this case, we are using “pmm” which stands for “Predictive Mean Matching”, because it is a flexible and widely used imputation method that works well with continuous variables. The method estimates the missing values by drawing from a set of observed values that have similar characteristics to the missing values.

# Impute missing data using mice
imputed_df <- mice(census_df, m = 5, maxit = 50, method = "pmm")

## 
##  iter imp variable
##   1   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   1   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   1   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   1   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   1   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   2   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   2   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   2   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   2   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   2   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   3   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   3   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   3   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   3   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   3   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   4   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   4   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   4   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   4   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   4   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   5   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   5   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   5   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   5   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   5   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   6   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   6   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   6   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   6   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   6   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   7   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   7   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   7   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   7   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   7   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   8   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   8   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   8   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   8   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   8   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   9   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   9   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   9   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   9   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   9   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   10   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   10   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   10   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   10   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   10   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   11   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   11   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   11   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   11   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   11   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   12   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   12   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   12   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   12   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   12   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   13   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   13   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   13   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   13   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   13   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   14   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   14   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   14   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   14   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   14   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   15   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   15   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   15   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   15   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   15   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   16   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   16   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   16   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   16   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   16   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   17   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   17   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   17   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   17   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   17   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   18   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   18   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   18   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   18   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   18   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   19   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   19   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   19   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   19   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   19   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   20   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   20   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   20   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   20   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   20   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   21   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   21   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   21   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   21   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   21   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   22   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   22   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   22   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   22   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   22   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   23   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   23   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   23   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   23   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   23   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   24   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   24   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   24   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   24   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   24   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   25   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   25   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   25   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   25   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   25   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   26   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   26   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   26   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   26   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   26   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   27   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   27   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   27   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   27   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   27   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   28   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   28   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   28   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   28   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   28   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   29   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   29   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   29   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   29   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   29   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   30   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   30   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   30   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   30   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   30   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   31   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   31   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   31   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   31   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   31   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   32   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   32   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   32   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   32   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   32   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   33   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   33   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   33   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   33   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   33   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   34   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   34   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   34   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   34   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   34   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   35   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   35   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   35   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   35   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   35   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   36   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   36   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   36   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   36   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   36   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   37   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   37   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   37   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   37   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   37   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   38   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   38   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   38   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   38   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   38   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   39   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   39   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   39   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   39   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   39   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   40   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   40   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   40   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   40   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   40   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   41   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   41   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   41   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   41   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   41   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   42   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   42   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   42   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   42   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   42   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   43   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   43   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   43   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   43   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   43   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   44   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   44   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   44   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   44   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   44   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   45   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   45   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   45   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   45   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   45   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   46   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   46   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   46   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   46   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   46   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   47   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   47   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   47   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   47   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   47   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   48   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   48   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   48   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   48   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   48   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   49   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   49   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   49   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   49   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   49   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   50   1  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   50   2  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   50   3  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   50   4  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021
##   50   5  pct_bb_2020  pct_bb_2021  pct_college_2020  pct_college_2021  pct_foreign_born_2020  pct_foreign_born_2021  pct_it_workers_2018  pct_it_workers_2020  pct_it_workers_2021  median_hh_inc_2018  median_hh_inc_2020  median_hh_inc_2021

## Warning: Number of logged events: 500

# Extract imputed data
imputed_data <- complete(imputed_df)

# Check for missing values in imputed data
colSums(is.na(imputed_data))

##           pct_bb_2017           pct_bb_2018           pct_bb_2019 
##                     0                     0                     0 
##           pct_bb_2020           pct_bb_2021                 cfips 
##                     0                     0                     0 
##      pct_college_2017      pct_college_2018      pct_college_2019 
##                     0                     0                     0 
##      pct_college_2020      pct_college_2021 pct_foreign_born_2017 
##                     0                     0                     0 
## pct_foreign_born_2018 pct_foreign_born_2019 pct_foreign_born_2020 
##                     0                     0                     0 
## pct_foreign_born_2021   pct_it_workers_2017   pct_it_workers_2018 
##                     0                     0                     0 
##   pct_it_workers_2019   pct_it_workers_2020   pct_it_workers_2021 
##                     0                     0                     0 
##    median_hh_inc_2017    median_hh_inc_2018    median_hh_inc_2019 
##                     0                     0                     0 
##    median_hh_inc_2020    median_hh_inc_2021 
##                     0                     0

print(imputed_data[missing_rows,])

##      pct_bb_2017 pct_bb_2018 pct_bb_2019 pct_bb_2020 pct_bb_2021 cfips
## 93          80.5        79.1        80.4        80.9        81.3  2261
## 1817        49.1        52.1        57.6        60.7        63.5 35039
## 2645        66.3        66.6        61.2        63.2        70.1 48243
## 2674        64.5        72.7        73.3        96.8        97.0 48301
##      pct_college_2017 pct_college_2018 pct_college_2019 pct_college_2020
## 93               23.1             19.0             16.5             15.9
## 1817             12.0             12.5             12.6             10.6
## 2645             18.4             16.0             10.8             14.3
## 2674              4.7              0.0              0.0              0.0
##      pct_college_2021 pct_foreign_born_2017 pct_foreign_born_2018
## 93               15.6                   4.9                   6.3
## 1817             10.1                   4.5                   3.7
## 2645             10.9                  22.4                  14.9
## 2674              0.0                  10.8                  15.7
##      pct_foreign_born_2019 pct_foreign_born_2020 pct_foreign_born_2021
## 93                     6.6                   3.9                   6.3
## 1817                   4.2                   4.5                   4.8
## 2645                  20.9                  10.1                  12.7
## 2674                  12.2                   0.0                   1.2
##      pct_it_workers_2017 pct_it_workers_2018 pct_it_workers_2019
## 93                   3.3                 3.9                 5.3
## 1817                 0.8                 0.5                 0.8
## 2645                 0.0                 0.0                 0.0
## 2674                 0.0                 0.0                 0.0
##      pct_it_workers_2020 pct_it_workers_2021 median_hh_inc_2017
## 93                   4.9                 5.1              86019
## 1817                 0.4                 0.7              33422
## 2645                 0.0                 0.0              46534
## 2674                 0.0                 0.0              80938
##      median_hh_inc_2018 median_hh_inc_2019 median_hh_inc_2020
## 93                82306              79867              71505
## 1817              33456              39952              42264
## 2645              53194              53088              42126
## 2674              81875              83750              44076
##      median_hh_inc_2021
## 93                73892
## 1817              46994
## 2645              38659
## 2674              52956

index <- unique(train_df$first_day_of_month)
print(index)

##  [1] "2019-08-01" "2019-09-01" "2019-10-01" "2019-11-01" "2019-12-01"
##  [6] "2020-01-01" "2020-02-01" "2020-03-01" "2020-04-01" "2020-05-01"
## [11] "2020-06-01" "2020-07-01" "2020-08-01" "2020-09-01" "2020-10-01"
## [16] "2020-11-01" "2020-12-01" "2021-01-01" "2021-02-01" "2021-03-01"
## [21] "2021-04-01" "2021-05-01" "2021-06-01" "2021-07-01" "2021-08-01"
## [26] "2021-09-01" "2021-10-01" "2021-11-01" "2021-12-01" "2022-01-01"
## [31] "2022-02-01" "2022-03-01" "2022-04-01" "2022-05-01" "2022-06-01"
## [36] "2022-07-01" "2022-08-01" "2022-09-01" "2022-10-01"

training data is from 08/2019 to 10/2022

index <- unique(test_df$first_day_of_month)
print(index)

## [1] "2022-11-01" "2022-12-01" "2023-01-01" "2023-02-01" "2023-03-01"
## [6] "2023-04-01" "2023-05-01" "2023-06-01"

prediction dates are from 11/2022 to 06/2023

Let’s Do EDA

To make analysis easier and be able to group the data by year and month, we’ll extract year and month values from the first_day_of_month column in both train and test dataframes using apply() method and lambda function, and then create new columns called year (int), month (int), and year_month (str) in each dataframe to store these values.

# Add year, month and year_month columns to train_df
train_df$year <- as.integer(substr(train_df$first_day_of_month, 1, 4))
train_df$month <- as.integer(substr(train_df$first_day_of_month, 6, 7))
train_df$year_month <- substr(train_df$first_day_of_month, 1, 7)

# Add year, month and year_month columns to test_df
test_df$year <- as.integer(substr(test_df$first_day_of_month, 1, 4))
test_df$month <- as.integer(substr(test_df$first_day_of_month, 6, 7))
test_df$year_month <- substr(test_df$first_day_of_month, 1, 7)

# Set variables of interest
vars <- c("pct_bb", "pct_college", "pct_foreign_born", "pct_it_workers", "median_hh_inc")

# Loop through variables and merge with train_df
merged_df <- train_df

for (var in vars) {
  # Select columns and pivot longer
  merged_df <- imputed_data %>%
    select(cfips, paste0(var, "_2017"):paste0(var, "_2020")) %>%
    pivot_longer(cols = starts_with(var),
                 names_to = "year",
                 values_to = var) %>%
    # Modify year and month columns
    mutate(year = as.integer(str_sub(year, -4)) + 2) %>%
    uncount(12, .id = "month") %>%
    mutate(month = month) %>%
    # Merge with merged_df
    merge(merged_df, by = c("cfips", "year", "month"), all.x = TRUE) %>%
    arrange(cfips, row_id) 
}
merged_df <- merged_df %>%
  select(row_id, cfips, county, state, first_day_of_month, microbusiness_density, active, year_month, year, month, pct_bb, pct_college, pct_foreign_born, pct_it_workers, median_hh_inc) %>%
  # remove NA values created because of the 
  na.omit(merged_df)

colSums(is.na(merged_df))

##                row_id                 cfips                county 
##                     0                     0                     0 
##                 state    first_day_of_month microbusiness_density 
##                     0                     0                     0 
##                active            year_month                  year 
##                     0                     0                     0 
##                 month                pct_bb           pct_college 
##                     0                     0                     0 
##      pct_foreign_born        pct_it_workers         median_hh_inc 
##                     0                     0                     0

str(train_df)

## 'data.frame':    122265 obs. of  10 variables:
##  $ row_id               : chr  "1001_2019-08-01" "1001_2019-09-01" "1001_2019-10-01" "1001_2019-11-01" ...
##  $ cfips                : int  1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
##  $ county               : chr  "Autauga County" "Autauga County" "Autauga County" "Autauga County" ...
##  $ state                : chr  "Alabama" "Alabama" "Alabama" "Alabama" ...
##  $ first_day_of_month   : chr  "2019-08-01" "2019-09-01" "2019-10-01" "2019-11-01" ...
##  $ microbusiness_density: num  3.01 2.88 3.06 2.99 2.99 ...
##  $ active               : int  1249 1198 1269 1243 1243 1242 1217 1227 1255 1257 ...
##  $ year                 : int  2019 2019 2019 2019 2019 2020 2020 2020 2020 2020 ...
##  $ month                : int  8 9 10 11 12 1 2 3 4 5 ...
##  $ year_month           : chr  "2019-08" "2019-09" "2019-10" "2019-11" ...

cat(rep("=", 40), "\n")

## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

str(test_df)

## 'data.frame':    25080 obs. of  6 variables:
##  $ row_id            : chr  "1001_2022-11-01" "1003_2022-11-01" "1005_2022-11-01" "1007_2022-11-01" ...
##  $ cfips             : int  1001 1003 1005 1007 1009 1011 1013 1015 1017 1019 ...
##  $ first_day_of_month: chr  "2022-11-01" "2022-11-01" "2022-11-01" "2022-11-01" ...
##  $ year              : int  2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 ...
##  $ month             : int  11 11 11 11 11 11 11 11 11 11 ...
##  $ year_month        : chr  "2022-11" "2022-11" "2022-11" "2022-11" ...

# Create plots
p1 <- train_df %>%
  # Group train_df by year_month
  group_by(year_month) %>%
  # calculate the mean value of microbusiness_density for each group
  summarise(mean_microbusiness_density = mean(microbusiness_density)) %>%
  ggplot(aes(x = year_month, y = mean_microbusiness_density, group = 1)) +
  geom_line() +
  labs(title = "Overall Average Microbusiness Density",
       x = "Year-Month",
       y = "Average Microbusiness Density") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

p2 <- train_df %>%
  group_by(year_month) %>%
  summarize(avg_active = mean(active)) %>%
  ggplot(aes(x = year_month, y = avg_active)) +
  geom_line(group = 1) +
  labs(title = "Overall Active Microbusiness Count",
       x = "Year-Month", 
       y = "Active") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))


# Display the plots
grid.arrange(p1, p2, nrow = 2)

# Group train_df by year and calculate the mean value of microbusiness_density for each group
train_df_mean_year <- train_df %>%
  group_by(year) %>%
  summarize(avg_microbusiness_density = mean(microbusiness_density))

# Group train_df by month and calculate the mean value of the target variable for each group
train_df_mean_month <- train_df %>%
  group_by(month) %>%
  summarize(avg_microbusiness_density = mean(microbusiness_density))

# Plot the monthly mean values
p1 <- 
  ggplot(train_df_mean_month, aes(x = month, y = avg_microbusiness_density)) +
  geom_line() +
  ggtitle("Avg Monthly Microbusiness Density") +
  xlab("Month") +
  ylab("Average Microbusiness Density") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Plot the yearly mean values
p2 <- 
  ggplot(train_df_mean_year, aes(x = year, y = avg_microbusiness_density)) +
  geom_line() +
  ggtitle("Avg Yearly Microbusiness Density") +
  xlab("Year") +
  ylab("Average Microbusiness Density") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Display the plots side by side
grid.arrange(p1, p2, ncol=2)

The Bureau of Economic Analysis (BEA) divides the United States into eight distinct economic regions. These regions are based on similarities in economic characteristics such as industry composition, income levels, and employment patterns. The eight regions are:

1. New England: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont.

   The economy in this region is largely based on manufacturing, healthcare, education, and finance.

2. Mideast: Delaware, Maryland, New Jersey, New York, Pennsylvania, and the District of Columbia.

   The region has a diverse economy, with a mix of manufacturing, finance, healthcare, and professional services.

3. Great Lakes: Illinois, Indiana, Michigan, Ohio, and Wisconsin.

   The region has a strong manufacturing base, particularly in the automotive industry, and also has a significant healthcare sector.

4. Plains: Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota.

   Agriculture and energy production are major industries in this region, along with manufacturing and healthcare.

5. Southeast: Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia.

   The Southeast has a diverse economy, with significant industries in healthcare, finance, and manufacturing, as well as tourism and agriculture.

6. Southwest: Arizona, New Mexico, Oklahoma, and Texas.

   The region has a strong energy sector, particularly in oil and gas production, and also has significant industries in manufacturing, healthcare, and finance.

7. Rocky Mountain: Colorado, Idaho, Montana, Utah, and Wyoming.

   The region is known for its natural resources, particularly in mining and energy production, as well as tourism, healthcare, and manufacturing.

8. Far West: Alaska, California, Hawaii, Nevada, Oregon, and Washington.

   This region has a diverse economy, with significant industries in technology, finance, healthcare, and manufacturing, as well as tourism and agriculture.

# Get the map of the United States
us_map <- map_data("state")

# Create a lookup table for state abbreviations and their corresponding full names
state_names <- data.frame(state = state.abb, name = tolower(state.name))

# Map the regions to the states
region_map <- us_map %>%
#left_join(state_names, by = c("region" = "state")) %>%
  left_join(state_names, by = c("region" = "name")) %>%
# merge(us_map, state_names, by.x=c("region"), by.y=c("name")) %>%
  mutate(region = 
           ifelse(region %in% c("connecticut", "maine", "massachusetts", "new hampshire", "rhode island", "vermont"), "New England",
                         ifelse(region %in% c("delaware", "maryland", "new jersey", "new york", "pennsylvania", "district of columbia"), "Mideast",
                                ifelse(region %in% c("illinois", "indiana", "michigan", "ohio", "wisconsin"), "Great Lakes",
                                       ifelse(region %in% c("iowa", "kansas", "minnesota", "missouri", "nebraska", "north dakota", "south dakota"), "Plains",
                                              ifelse(region %in% c("alabama", "arkansas", "florida", "georgia", "kentucky", "louisiana", "mississippi", "north carolina", "south carolina", "tennessee", "virginia", "west virginia"), "Southeast",
                                                     ifelse(region %in% c("arizona", "new mexico", "oklahoma", "texas"), "Southwest",
                                                            ifelse(region %in% c("colorado", "idaho", "montana", "utah", "wyoming"), "Rocky Mountain",
                                                                   ifelse(region %in% c("alaska", "california", "hawaii", "nevada", "oregon", "washington"), "Far West", NA
                                                                          )
                                                                   )
                                                            )
                                                     )
                                              )
                                       )
                                )
                         )
         )

# Summarize the data to get the center coordinates of each state
#state_centers <- region_map %>% 
#  group_by(state) %>% 
#  summarise(long = mean(long), lat = mean(lat))
# add labels
states <- aggregate(cbind(long, lat) ~ region, data=us_map, 
                FUN=function(x)mean(range(x)))
states$group <- c("AL", "AR", "AZ", "CA", "CO", "CT", "DE", "DC", "FL", "GA", "IA", 
              "ID", "IL", "IN", "KS", "KY", "LA", "MA", "MD", "ME", "MI", "MN", 
              "MO", "MS", "MT", "NC", "ND", "NE", "NH", "NJ", "NM", "NV", "NY", 
              "OH", "OK", "OR", "PA", "RI", "SC", "SD", "TN", "TX", "UT", "VA", 
              "VT", "WA", "WI", "WV", "WY")

# names(states)[names(states) == "region"] <- "group"


#Plot the map
ggplot(region_map, aes(x = long, y = lat, group = group, fill = region)) +
  geom_polygon(color = "black", show.legend = TRUE) +
#  geom_text(aes(label = state), data = region_map, size = 3, vjust = 2, hjust = 2) +
#  geom_text(aes(label = state), data = state_centers, size = 2, vjust = 2, hjust = 2) +
  geom_text(data = states, aes(long, lat, label = group), size = 2.5, inherit.aes = FALSE, color = "white", fontface = "bold") +
#  scale_fill_gradient(low = "white", high = "darkred") +
#  scale_fill_manual(values = viridis(n = 60), na.value = "gray") +
  labs(title = "Bureau of Economic Analysis Regional Divisions Map", fill = "Region") +
#  geom_text(aes(x = long, y = lat, label = state), data = state_centers, size = 3, color = "white") +
  theme_void() +
  theme(panel.background = element_rect(fill = "gray75", color = NA))

str(states)

## 'data.frame':    49 obs. of  4 variables:
##  $ region: chr  "alabama" "arizona" "arkansas" "california" ...
##  $ long  : num  -86.7 -111.9 -92.1 -119.3 -105.6 ...
##  $ lat   : num  32.6 34.2 34.8 37.3 39 ...
##  $ group : chr  "AL" "AR" "AZ" "CA" ...

First, we need to convert state and county columns in train_df to lowercase letters. Because, the data from map_data() will be in lowercase and when merging two dataframes it might cause problems.

# Convert state and county columns in train_df to lowercase
train_df <- train_df %>% 
  mutate(state = tolower(state)) %>%
  mutate(county = tolower(county))

Then, we’ll create a new column named region and assign region values based on state column:

# Create a new column named region and initialize all values as NA
train_df$region <- NA

# Assign region values based on state column
for (i in 1:nrow(train_df)) {
  if (train_df$state[i] %in% c("connecticut", "maine", "massachusetts", "new hampshire", "rhode island", "vermont")) {
    train_df$region[i] <- "new england"
  } else if (train_df$state[i] %in% c("delaware", "maryland", "new jersey", "new york", "pennsylvania", "district of columbia")) {
    train_df$region[i] <- "mideast"
  } else if (train_df$state[i] %in% c("illinois", "indiana", "michigan", "ohio", "wisconsin")) {
    train_df$region[i] <- "great lakes"
  } else if (train_df$state[i] %in% c("iowa", "kansas", "minnesota", "missouri", "nebraska", "north dakota", "south dakota")) {
    train_df$region[i] <- "plains"
  } else if (train_df$state[i] %in% c("alabama", "arkansas", "florida", "georgia", "kentucky", "louisiana", "mississippi", "north carolina", "south carolina", "tennessee", "virginia", "west virginia")) {
    train_df$region[i] <- "southeast"
  } else if (train_df$state[i] %in% c("arizona", "new mexico", "oklahoma", "texas")) {
    train_df$region[i] <- "southwest"
  } else if (train_df$state[i] %in% c("colorado", "idaho", "montana", "utah", "wyoming")) {
    train_df$region[i] <- "rocky mountain"
  } else if (train_df$state[i] %in% c("alaska", "california", "hawaii", "nevada", "oregon", "washington")) {
    train_df$region[i] <- "far west"
  } else {
    train_df$region[i] <- "other"
  }
}

# Print all the unique values in the region column
unique(train_df$region)

## [1] "southeast"      "far west"       "southwest"      "rocky mountain"
## [5] "new england"    "mideast"        "great lakes"    "plains"

# Group train_df by region and calculate average microbusiness density
train_df %>%
  group_by(region) %>%
  summarize(avg_density = mean(microbusiness_density)) %>%

  # Create bar plot of average density by region
  ggplot(aes(x = region, y = avg_density)) +
  geom_bar(stat = "identity") +
  
  # Add plot title and axis labels
  labs(title = "Average Microbusiness Density Per Region",
       x = "Region", y = "Avg Density") +
  
  # Apply a black and white theme to the plot
  theme_bw()

# Group train_df by region and calculate average microbusiness density
avg_density <- train_df %>%
  group_by(region) %>%
  summarize(avg_density = mean(microbusiness_density))

# Create a lookup table for state abbreviations and their corresponding full names
state_names <- data.frame(state = state.abb, name = tolower(state.name))

# Lowercase region column of region_map
region_map <- region_map %>% 
  mutate(region = tolower(region)) 

# Merge the average density data with the region_map data
plot_data <- merge(region_map, avg_density, by = "region") %>%
  arrange(order)

# Coordinates of the center of regions
bea_regions <- data.frame(
  group = c("New England", "Mideast", "Great Lakes", "Plains", 
             "Southeast", "Southwest", "Rocky Mountain", "Far West"),
  x = c(-71.8, -76.9, -86.6, -98.5, -82.4, -106.4, -111.1, -119.8),
  y = c(42.2, 39, 43.4, 39.8, 32.6, 34.3, 44.4, 38.4)
)

# Create the plot
ggplot(plot_data, aes(x = long, y = lat, group = group, fill = avg_density)) +
  geom_polygon(color = "black") +
  geom_label(data = bea_regions,
             aes(x = x, y = y, label = group),
             size = 3, fontface = "bold", 
             label.padding = unit(0.2, "lines"),
             label.size = 0.2,
             fill = "gray75", color = "black") +
  scale_fill_gradient(low = "white", high = "darkred") +
  # scale_fill_viridis(name = "Avg Density", na.value = "gray") +
  labs(title = "Average Microbusiness Density Per Region", fill = "Avg Density") +
  theme_void()

# Aggregate microbusiness density by state
state_avg <- aggregate(microbusiness_density ~ state, data = train_df, FUN = mean)

# Load US map data
us_map <- map_data("state")

# Merge state_avg with us_map based on region and state
map_data <- merge(us_map, state_avg, by.x = "region", by.y = "state")

# Create a heatmap of microbusiness density by state
ggplot(map_data, aes(x = long, y = lat, group = group, fill = microbusiness_density)) +
  geom_polygon() +
  scale_fill_gradient(low = "white", high = "navyblue") +
  coord_map() +
  labs(title = "Average Microbusiness Density per State", fill = "Density") +
  theme_void() +
  theme(panel.background = element_rect(fill = "lightblue", color = NA))

#{r fig.width = 10 ,fig.height = 12, out.width='100%', fig.align='center'}
# Aggregate microbusiness density by county

#county_avg <- train_df %>%
#  group_by(cfips, county) %>%
#  summarise(microbusiness_density = mean(microbusiness_density))

county_avg <- aggregate(microbusiness_density ~ county + state, data = train_df, FUN = mean)
county_avg$county <- gsub(" county", "", county_avg$county)
county_avg$county <- gsub(" city", "", county_avg$county)
county_avg$county <- gsub(" parish", "", county_avg$county)


# Load US county map data
us_map <- map_data("county")

# Merge county_avg with us_map based on region and county
map_data <- merge(us_map, county_avg, by.x = c("subregion", "region"), by.y = c("county", "state")) %>%
  arrange(order)

# Create a heatmap of microbusiness density by county using ggplot2
ggplot(map_data, aes(x = long, y = lat, group = group, fill = microbusiness_density)) +
  geom_polygon() +
  scale_fill_gradient(low = "lightblue", high = "navyblue") +
  coord_map() +
  labs(title = "Average Microbusiness Density per County", fill = "Density") +
  theme_void() +
  theme(panel.background = element_rect(fill = "gray85", color = NA))

str(merged_df)

## 'data.frame':    122265 obs. of  15 variables:
##  $ row_id               : chr  "1001_2019-08-01" "1001_2019-09-01" "1001_2019-10-01" "1001_2019-11-01" ...
##  $ cfips                : int  1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
##  $ county               : chr  "Autauga County" "Autauga County" "Autauga County" "Autauga County" ...
##  $ state                : chr  "Alabama" "Alabama" "Alabama" "Alabama" ...
##  $ first_day_of_month   : chr  "2019-08-01" "2019-09-01" "2019-10-01" "2019-11-01" ...
##  $ microbusiness_density: num  3.01 2.88 3.06 2.99 2.99 ...
##  $ active               : int  1249 1198 1269 1243 1243 1242 1217 1227 1255 1257 ...
##  $ year_month           : chr  "2019-08" "2019-09" "2019-10" "2019-11" ...
##  $ year                 : num  2019 2019 2019 2019 2019 ...
##  $ month                : int  8 9 10 11 12 1 2 3 4 5 ...
##  $ pct_bb               : num  76.6 76.6 76.6 76.6 76.6 78.9 78.9 78.9 78.9 78.9 ...
##  $ pct_college          : num  14.5 14.5 14.5 14.5 14.5 15.9 15.9 15.9 15.9 15.9 ...
##  $ pct_foreign_born     : num  2.1 2.1 2.1 2.1 2.1 2 2 2 2 2 ...
##  $ pct_it_workers       : num  1.3 1.3 1.3 1.3 1.3 1.1 1.1 1.1 1.1 1.1 ...
##  $ median_hh_inc        : num  55317 55317 55317 55317 55317 ...
##  - attr(*, "na.action")= 'omit' Named int [1:28551] 40 41 42 43 44 45 46 47 48 88 ...
##   ..- attr(*, "names")= chr [1:28551] "40" "41" "42" "43" ...

Boxplots are a visualization tool that provide insights into the central tendency and spread of a dataset, as well as identify outliers and skewness. They are useful for detecting anomalies and comparing variable distributions in a dataset, providing valuable insights into data distribution for exploratory data analysis.

boxplot(merged_df$microbusiness_density, col = "blue", main = "Microbusiness Density")

par(mfrow=c(3,2)) # set plot layout to 3 rows and 2 columns
boxplot(merged_df$median_hh_inc, col = "pink", main = "Median Household Income")
boxplot(merged_df$pct_college, col = "pink", main = "Percentage with College Education")
boxplot(merged_df$pct_foreign_born, col = "pink", main = "Percentage of Foreign-born Residents")
boxplot(merged_df$pct_it_workers, col = "pink", main = "Percentage of IT Workers")
boxplot(merged_df$pct_bb, color = "pink", main = "Percentage of Broadband Access")
boxplot(merged_df$active, color = "pink", main = "Active Microbusiness Count")

Outlier detection:

Outlier detection is an important step in data analysis, as outliers can significantly affect the results of statistical analyses. One method for outlier detection is the decision range approach.

The decision range approach involves setting a range of values outside of which any observations are considered outliers. The decision range is determined based on the data distribution and the researcher’s judgment. One common approach is to use the interquartile range (IQR) to define the decision range. The IQR is calculated as the difference between the third quartile (Q3) and the first quartile (Q1) of the data.

The decision range is then defined as the range from Q1 - 1.5IQR to Q3 + 1.5IQR. Any observations that fall outside of this range are considered outliers. This method is useful for identifying potential outliers in a dataset and can help to ensure that statistical analyses are robust and accurate.

quartiles <- quantile(merged_df$microbusiness_density, probs = seq(0, 1, 0.25), na.rm = FALSE,
         names = TRUE, type = 7, digits = 6)
quartiles

##         0%        25%        50%        75%       100% 
##   0.000000   1.639344   2.586543   4.519231 284.340030

# Calculate the mean and standard deviation of 'microbusiness_density'
mean_density <- mean(merged_df$microbusiness_density)
sd_density <- sd(merged_df$microbusiness_density)

# Create a range of values for the x-axis
x_values <- seq(mean_density - 3*sd_density, mean_density + 3*sd_density, length.out = 1000)

# Create a bell curve using the 'dnorm' function with mean and standard deviation calculated above
y_values <- dnorm(x_values, mean = mean_density, sd = sd_density)

# Combine the 'x_values' and 'y_values' into a data frame using the 'data.frame' function
density_df <- data.frame(x = x_values, y = y_values)

# Create a boxplot using the 'ggplot' function from 'ggplot2' package
boxplot <- ggplot(data = merged_df, aes(x = "", y = merged_df$microbusiness_density)) +
  geom_boxplot(fill = "skyblue") +
  labs(x = "", y = "Microbusiness Density") +
  ggtitle("Boxplot for Microbusiness Density")

# Create a Q-Q plot using the 'ggplot' function from 'ggplot2' package and add a diagonal line using the 'geom_abline' function
qqplot <- ggplot(data = density_df, aes(sample = x)) +
  geom_qq() +
  geom_abline(intercept = 0, slope = 1, colour = "red") +
  labs(x = "Theoretical Normal Quantiles", y = "Observed Quantiles") +
  ggtitle("Q-Q Plot for Microbusiness Density")

# Create a bell curve using the 'ggplot' function from 'ggplot2' package and add the bell curve using the 'geom_line' function
densityplot <- ggplot(data = merged_df, aes(x = microbusiness_density)) +
  geom_histogram(aes(y = ..density..), bins = 30, colour = "black", fill = "white") +
  geom_line(data = density_df, aes(x = x, y = y), colour = "red", size = 1) +
  labs(x = "Microbusiness Density", y = "Density") +
  ggtitle("Distribution of Microbusiness Density")

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.

# Arrange the plots in one row using the 'grid.arrange' function from the 'gridExtra' package
grid.arrange(densityplot, boxplot, qqplot, ncol = 3, widths = c(2, 1, 2))

## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.

par(mfrow=c(1,3)) # set plot layout to 3 rows and 2 columns
mean_density <- mean(merged_df$microbusiness_density)
sd_density <- sd(merged_df$microbusiness_density)
x_values <- seq(mean_density - 3*sd_density, mean_density + 3*sd_density, length.out = 1000)
y_values <- dnorm(x_values, mean = mean_density, sd = sd_density)
density_df <- data.frame(x = x_values, y = y_values)
ggplot(merged_df, aes(x = microbusiness_density)) +
  geom_histogram(aes(y = ..density..), bins = 30, colour = "black", fill = "lightblue") +
  geom_line(data = density_df, aes(x = x, y = y), colour = "navy", linewidth = 1) +
  labs(x = "Microbusiness Density", y = "Density") +
  ggtitle("Distribution of Microbusiness Density") +
  theme_minimal()

theoretical_norm <- qnorm(p = seq(0, 1, length.out = length(merged_df$microbusiness_density)), mean = mean(merged_df$microbusiness_density), sd = sd(merged_df$microbusiness_density))
density_df <- data.frame(observed = sort(merged_df$microbusiness_density), theoretical = theoretical_norm)
ggplot(density_df, aes(x = theoretical, y = observed)) +
  geom_point() +
  geom_abline(intercept = 0, slope = 1, colour = "red") +
  labs(x = "Theoretical Normal Quantiles", y = "Observed Quantiles") +
  ggtitle("Q-Q Plot for Microbusiness Density")

ggplot(data = merged_df, aes(x = "", y = microbusiness_density)) +
  geom_boxplot(fill = "skyblue") +
  labs(x = "", y = "Microbusiness Density") +
  ggtitle("Boxplot for Microbusiness Density")

# compute the correlation matrix
corr_matrix <- cor(imputed_data)

# visualize the correlation matrix using corrplot
corrplot(corr_matrix, type = "upper", method = "circle")

library(ggplot2)
library(reshape2)

## 
## Attaching package: 'reshape2'

## The following object is masked from 'package:tidyr':
## 
##     smiths

# Calculate the correlation matrix
cor_mat <- cor(imputed_data)

# Melt the correlation matrix to a long format
melted_cor <- melt(cor_mat)

# Generate the heatmap
ggplot(melted_cor, aes(x=Var1, y=Var2, fill=value)) + 
  geom_tile() +
  scale_fill_gradient2(low = "blue", high = "red", mid = "white", 
                       midpoint = 0, limit = c(-1,1), space = "Lab", 
                       name="Pearson\nCorrelation") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, 
                                   size = 8, hjust = 1),
        axis.text.y = element_text(size = 8),
        axis.title = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        legend.justification = c(1, 0),
        legend.position = c(0.8, 0.2),
        legend.direction = "horizontal") 

str(imputed_data)

## 'data.frame':    3142 obs. of  26 variables:
##  $ pct_bb_2017          : num  76.6 74.5 57.2 62 65.8 49.4 58.2 71 62.8 67.5 ...
##  $ pct_bb_2018          : num  78.9 78.1 60.4 66.1 68.5 58.9 62.1 73 66.5 68.6 ...
##  $ pct_bb_2019          : num  80.6 81.8 60.5 69.2 73 60.1 64.6 75.1 69.4 70.7 ...
##  $ pct_bb_2020          : num  82.7 85.1 64.6 76.1 79.6 60.6 73.6 79.8 74.5 75 ...
##  $ pct_bb_2021          : num  85.5 87.9 64.6 74.6 81 59.4 76.3 81.6 77.1 76.7 ...
##  $ cfips                : int  1001 1003 1005 1007 1009 1011 1013 1015 1017 1019 ...
##  $ pct_college_2017     : num  14.5 20.4 7.6 8.1 8.7 6.6 9.6 10.2 9 6.6 ...
##  $ pct_college_2018     : num  15.9 20.7 7.8 7.6 8.1 7.4 9.7 10.2 9.3 6.8 ...
##  $ pct_college_2019     : num  16.1 21 7.6 6.5 8.6 7.4 9.7 10.5 9.5 6.6 ...
##  $ pct_college_2020     : num  16.7 20.2 7.3 7.4 8.9 6.1 10.1 10.5 10.5 6.3 ...
##  $ pct_college_2021     : num  16.4 20.6 6.7 7.9 9.3 8.1 8.1 11.4 9.6 6.2 ...
##  $ pct_foreign_born_2017: num  2.1 3.2 2.7 1 4.5 1.8 1 2.6 1.3 0.7 ...
##  $ pct_foreign_born_2018: num  2 3.4 2.5 1.4 4.4 0.9 1.4 2.7 1.4 0.8 ...
##  $ pct_foreign_born_2019: num  2.3 3.7 2.7 1.5 4.5 0.7 0.8 2.7 1.8 0.9 ...
##  $ pct_foreign_born_2020: num  2.3 3.4 2.6 1.6 4.4 1.5 1.9 2.5 1.9 1.9 ...
##  $ pct_foreign_born_2021: num  2.1 3.5 2.6 1.1 4.5 1.2 1.7 2.5 2 2 ...
##  $ pct_it_workers_2017  : num  1.3 1.4 0.5 1.2 1.3 0.4 1.1 1.4 2.4 1.4 ...
##  $ pct_it_workers_2018  : num  1.1 1.3 0.3 1.4 1.4 0.3 1.4 1.4 2.1 1.3 ...
##  $ pct_it_workers_2019  : num  0.7 1.4 0.8 1.6 0.9 0.5 1.7 1.2 2.1 1.2 ...
##  $ pct_it_workers_2020  : num  0.6 1 1.1 1.7 1.1 0.3 1.3 1 2.3 0.9 ...
##  $ pct_it_workers_2021  : num  1.1 1.3 0.8 2.1 0.9 0.2 1.4 1 1.8 0.4 ...
##  $ median_hh_inc_2017   : int  55317 52562 33368 43404 47412 29655 36326 43686 37342 40041 ...
##  $ median_hh_inc_2018   : num  58786 55962 34186 45340 48695 ...
##  $ median_hh_inc_2019   : int  58731 58320 32525 47542 49358 37785 40688 47255 42289 41919 ...
##  $ median_hh_inc_2020   : num  57982 61756 34990 51721 48922 ...
##  $ median_hh_inc_2021   : num  62660 64346 36422 54277 52830 ...

# Bar Plot
ggplot(imputed_data, aes(x=cfips, y=median_hh_inc_2017)) +
  geom_bar(stat="identity", fill="steelblue") +
  labs(title = "Median Household Income in 2017",
       x = "County FIPS Code", y = "Median Household Income")

# Pie Chart
ggplot(imputed_data, aes(x="", y=pct_foreign_born_2017, fill=cfips)) +
  geom_bar(width = 1, stat = "identity") +
  coord_polar("y", start=0) +
  labs(title = "Percentage of Foreign-born Population in 2017",
       fill = "County FIPS Code")

#creating a boxplot of the 'first_interval' column of the CustomerChurn_temp dataframe, grouped by the 'first_interval_label' column.
ggplot(merged_df, aes("", microbusiness_density)) +
  geom_boxplot()+scale_fill_manual(values = c("0" = "#009999","1" = "#CC3300"))

#Outlier Detection using decision range:
#Any data point lies outside of this range is considered as outliers
#We have lower bound and upper bound. any data outside of these 2 bounds will be considered as outliers. 
#Here we are just going to consider to consider the upper bound, as we can't able to see any outliers in lower bound region.

#Upper Bound: (Q3 + 1.5 * IQR)

#Finding the Quartiles(2nd Quartile and 3rd quartile)
quartiles <- quantile(train_df$microbusiness_density, probs=c(.25, .75), na.rm = FALSE)
#quartiles

#getting the IQR
iqr<-IQR(train_df$microbusiness_density)
#upper bound
second_interval_ub <- quartiles[2] + 1.5*iqr 
second_interval_ub

##      75% 
## 8.839061

#now we can label the second_interval based on upper bound
train_df$microbusiness_density_label<-as.numeric(train_df$microbusiness_density > second_interval_ub)

# Create the plot
ggplot(merged_df, aes(x = "", y = microbusiness_density)) +
  geom_boxplot(fill = "lightblue") +
  #labs(title = "doesn;t matter",
  #     x = NULL, y = NULL) +
  theme_minimal()

library(ggplot2)

# Create the plot
ggplot(imputed_data, aes(x = "", y = pct_bb_2021)) +
  geom_boxplot(fill = "lightblue") +
  labs(title = "Percentage of Population with Broadband Access in 2021",
       x = NULL, y = "Percentage") +
  theme_minimal()

# Create the box plot
ggplot(imputed_data, aes(x = "", y = pct_bb_2017)) +
  geom_boxplot(fill = "lightblue", color = "blue") +
  labs(title = "Percentage of Broadband Access in 2017", y = "Percentage") +
  theme_bw()

# Scatter Plot
ggplot(imputed_data, aes(x=median_hh_inc_2017, y=pct_it_workers_2017)) +
  geom_point() +
  labs(title = "Percentage of IT Workers and Median Household Income in 2017",
       x = "Median Household Income", y = "Percentage of IT Workers")

# Heatmap
imputed_data_long <- melt(imputed_data, id.vars="cfips", 
                          measure.vars=c("pct_bb_2017", "pct_bb_2018", "pct_bb_2019", 
                                         "pct_bb_2020", "pct_bb_2021"), 
                          variable.name="year", value.name="pct_bb")

ggplot(imputed_data_long, aes(x=cfips, y=year, fill=pct_bb)) +
  geom_tile() +
  scale_fill_gradient(low="white", high="steelblue") +
  labs(title = "Percentage of Broadband Coverage",
       x = "County FIPS Code", y = "Year")

us.map <-  map_data('state')

# add PADD zones
us.map$PADD[us.map$region %in% 
          c("maine", "vermont", "new hampshire", "massachusetts", "connecticut", "rhode island",
            "new york", "pennsylvania", "new jersey", "delaware", "district of columbia", "maryland",
            "west virginia", "virginia", "north carolina", "south carolina", "georgia", "florida")] <- "PADD 1: East Coast"
us.map$PADD[us.map$region %in% 
          c("south dakota", "north dakota","nebraska", "kansas", "oklahoma", 
            "minnesota", "iowa", "missouri", "wisconsin", "illinois", "indiana",
            "michigan", "ohio", "kentucky", "tennessee")] <- "PADD 2: Midwest"
us.map$PADD[us.map$region %in% 
          c("new mexico", "texas", "arkansas", "louisiana", "alabama", "mississippi")] <- "PADD 3: Gulf Coast"
us.map$PADD[us.map$region %in% 
          c("montana", "idaho", "wyoming", "utah", "colorado")] <- "PADD 4: Rocky Mountain"
us.map$PADD[us.map$region %in% 
          c("washington", "oregon", "nevada", "arizona", "california")] <- "PADD 5: West Coast"

# subset the dataframe by padd zones and move lat/lon accordingly
us.map$lat.transp[us.map$PADD == "PADD 1: East Coast"] <- us.map$lat[us.map$PADD == "PADD 1: East Coast"]
us.map$long.transp[us.map$PADD == "PADD 1: East Coast"] <- us.map$long[us.map$PADD == "PADD 1: East Coast"] + 5

us.map$lat.transp[us.map$PADD == "PADD 2: Midwest"] <- us.map$lat[us.map$PADD == "PADD 2: Midwest"]
us.map$long.transp[us.map$PADD == "PADD 2: Midwest"] <- us.map$long[us.map$PADD == "PADD 2: Midwest"]

us.map$lat.transp[us.map$PADD == "PADD 3: Gulf Coast"] <- us.map$lat[us.map$PADD == "PADD 3: Gulf Coast"] - 3
us.map$long.transp[us.map$PADD == "PADD 3: Gulf Coast"] <- us.map$long[us.map$PADD == "PADD 3: Gulf Coast"]

us.map$lat.transp[us.map$PADD == "PADD 4: Rocky Mountain"] <- us.map$lat[us.map$PADD == "PADD 4: Rocky Mountain"]
us.map$long.transp[us.map$PADD == "PADD 4: Rocky Mountain"] <- us.map$long[us.map$PADD == "PADD 4: Rocky Mountain"] - 5

us.map$lat.transp[us.map$PADD == "PADD 5: West Coast"] <- us.map$lat[us.map$PADD == "PADD 5: West Coast"] - 2
us.map$long.transp[us.map$PADD == "PADD 5: West Coast"] <- us.map$long[us.map$PADD == "PADD 5: West Coast"] - 10

# add labels
states <- aggregate(cbind(long.transp, lat.transp) ~ region, data=us.map, 
                FUN=function(x)mean(range(x)))
states$labels <- c("AL", "AR", "AZ", "CA", "CO", "CT", "DE", "DC", "FL", "GA", "IA", 
              "ID", "IL", "IN", "KS", "KY", "LA", "MA", "MD", "ME", "MI", "MN", 
              "MO", "MS", "MT", "NC", "ND", "NE", "NH", "NJ", "NM", "NV", "NY", 
              "OH", "OK", "OR", "PA", "RI", "SC", "SD", "TN", "TX", "UT", "VA", 
              "VT", "WA", "WI", "WV", "WY")



# plot and use padd zone as fill
ggplot(us.map,  aes(x=long.transp, y=lat.transp), colour="white") + 
  geom_polygon(aes(group = group, fill=PADD)) +
  geom_text(data=states, aes(long.transp, lat.transp, label=labels), size=3) +
  theme(panel.background = element_blank(),  # remove background
    panel.grid = element_blank(), 
    axis.line = element_blank(), 
    axis.title = element_blank(),
    axis.ticks = element_blank(),
    axis.text = element_blank()) +
  coord_equal()

# Create a new column named "subregion" and initialize all values as NA
us_map$subregion <- NA

# Assign subregion values based on region column
for (i in 1:nrow(us_map)) {
  if (us_map$region[i] %in% c("connecticut", "maine", "massachusetts", "new hampshire", "rhode island", "vermont")) {
    us_map$subregion[i] <- "new england"
  } else if (us_map$region[i] %in% c("delaware", "maryland", "new jersey", "new york", "pennsylvania", "district of columbia")) {
    us_map$subregion[i] <- "mideast"
  } else if (us_map$region[i] %in% c("illinois", "indiana", "michigan", "ohio", "wisconsin")) {
    us_map$subregion[i] <- "great lakes"
  } else if (us_map$region[i] %in% c("iowa", "kansas", "minnesota", "missouri", "nebraska", "north dakota", "south dakota")) {
    us_map$subregion[i] <- "plains"
  } else if (us_map$region[i] %in% c("alabama", "arkansas", "florida", "georgia", "kentucky", "louisiana", "mississippi", "north carolina", "south carolina", "tennessee", "virginia", "west virginia")) {
    us_map$subregion[i] <- "southeast"
  } else if (us_map$region[i] %in% c("arizona", "new mexico", "oklahoma", "texas")) {
    us_map$subregion[i] <- "southwest"
  } else if (us_map$region[i] %in% c("colorado", "idaho", "montana", "utah", "wyoming")) {
    us_map$subregion[i] <- "rocky Mountain"
  } else if (us_map$region[i] %in% c("alaska", "california", "hawaii", "nevada", "oregon", "washington")) {
    us_map$subregion[i] <- "far west"
  } else {
    us_map$subregion[i] <- "other"
  }
}